Evolutionary-Based Control Approaches for Multirobot Systems

In this chapter, we summarize and synthesize our investigations of the use of evolutionary algorithms to automatically program robots, particularly for application to space exploration. In the Space Robotics Group at the University of Toronto Institute for Aerospace Studies, we were motivated to begin work in this area a decade ago when the concept of network science became popular in the space exploration community. Network science commonly refers to science that requires a distribution of possibly simultaneous measurement devices or a distribution of platforms on a planetary body. Consider, for example, seismology studies of an alien body that will require sending a signal from one point on the surface to be read at several other points in order to analyze the material characteristics of the body. Or, consider the development of a very-low frequency array (VLFA) on the Moon to allow for heretofore unattainable astrophysical observations using radio astronomy. Such an observatory will require a number of dipole units deployed over a region of a few hundred square kilometres. Our original thoughts were that these and other network science experiments could be implemented using a network of small mobile robots, similar to a colony of ants. It is possible for millions of ants to act as a superorganism through local pheromone communication. Thomas (1974) perhaps describes this phenomenon best: